Latency Trumps All

Latency Trumps AllChris Saaritwitter.com/[email protected]

Thursday, November 19, 2009

http://blog.chrissaari.com

http://blog.chrissaari.com

mailto:[email protected]

mailto:[email protected]

Packet Latency

Time for a packet to get between points A and B Physical distance + time queued in devices along the way

~60ms


...


Anytime...

... the system is waiting for data The system is end to end- Human response time- Network card buffering- System bus/interconnect speed- Interrupt handling- Network stacks- Process scheduling delays- Application process waiting for data from memory to get

to CPU, or from disk to memory to CPU- Routers, modems, last mile speeds- Backbone speed and operating condition- Inter-cluster/colo performance


Big Picture

CPU

Net

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Mem

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Dis

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Tubes?


Latency vs. Bandwidth

Latency

BandwidthBits / Second

Time


Bandwidth of a Truck Full of Tape


Latency Lags Bandwidth -David Patterson

Given the record ofadvances in bandwidth ver-sus latency, the logicalquestion is why? Here arefive technical reasons andone marketing reason.

1. Moore’s Law helpsbandwidth more thanlatency. The scaling ofsemiconductor processesprovides both faster transis-tors and many more on achip. Moore’s Law predictsa periodic doubling in thenumber of transistors perchip, due to scaling and inpart to larger chips;recently, that rate has been22–24 months [6]. Band-width is helped by fastertransistors, more transis-tors, and more pins operat-ing in parallel. The fastertransistors help latency, butthe larger number of tran-sistors and the relativelylonger distances on theactually larger chips limitthe benefits of scaling tolatency. For example,processors in Table 1 grew by more than a factor of300 in transistors, and by more than a factor of 6 inpins, but area increased by almost a factor of 5. Sincedistance grows by the square root of the area, distancein Table 1 doubled.

2. Distance limits latency. Distance sets a lowerbound to latency. The delay on the long word linesand bit lines are the largest part of the row access timeof a DRAM. The speed of light tells us that if theother computer on the network is 300 meters away, itslatency can never be less than one microsecond.

3. Bandwidth is generally easier to sell. The non-technical reason that latencylags bandwidth is the marketingof performance: it is easier tosell higher bandwidth than tosell lower latency. For example,the benefits of a 10Gbps band-width Ethernet are likely easierto explain to customers todaythan a 10-microsecond latency

Ethernet, no matter whichactually provides bettervalue. One can argue thatgreater advances in band-width led to marketingtechniques to sell band-width that in turn trainedcustomers to desire it. Nomatter what the real chainof events, unquestionablyhigher bandwidth forprocessors, memories, orthe networks is easier tosell today than latency.Since bandwidth sells,engineering resources tendto be thrown at band-width, which further tipsthe balance.

4. Latency helps band-width. Technology im-provements that helplatency usually also helpbandwidth, but not viceversa. For example,

DRAM latency determines the number of accesses persecond, so lower latency means more accesses per sec-ond and hence higher bandwidth. Also, spinningdisks faster reduces the rotational latency, but the readhead must read data at the new faster rate as well.Thus, spinning the disk faster improves both band-width and rotational latency. However, increasing thelinear density of bits per inch on a track helps band-width but offers no help to latency.

5. Bandwidth hurts latency. It is often easy toimprove bandwidth at the expense of latency. Queuingtheory quantifies how buffers help bandwidth buthurt latency. As a second example, adding chips towiden a memory module increases bandwidth but thehigher fan-out on address lines may increase latency.

6. Operating system overhead hurts latency. Auser program that wants to send a message invokes the

COMMUNICATIONS OF THE ACM October 2004/Vol. 47, No. 10 73

Figure 1. Log-log plot of bandwidth and latency

milestones from Table 1 relative to the first milestone.

Table 2. Summary of annual improvements in latency, capacity, and bandwidth in Table 1.


The Problem

Relative Data Access Latencies, Fastest to Slowest- CPU Registers (1)- L1 Cache (1-2)- L2 Cache (6-10)- Main memory (25-100)

- Hard drive (1e7)- LAN (1e7-1e8)- WAN (1e9-2e9)

--- don’t cross this line, don’t go off mother board! ---


Relative Data Access Latency

CPU Register L1 L2 RAM

Fast Slow



CPU Register L1 L2 RAM Hard Disk

Fast Slow



Register L1 L2 RAM Hard Disk LANFloppy/CD-ROMWAN

Lower Higher


CPU Register

CPU Register Latency - Average Human Height


L1 Cache


L2 Cache

x 6 x 10


RAM

x 25 to x 100


Hard Drive

0.4 x equatorial circumference of Earth

x 10 M


WAN

x 100 M

0.42 x Earth to Moon Distance


To experience pain...

Mobile phone network latency 2-10x that of wired- iPhone 3G 500ms ping

x 500 M

2 x Earth to Moon Distance


500ms isn’t that long...


Google SPDY

“It is designed specifically for minimizing latency through features such as multiplexed streams, request prioritization and HTTP header compression.”


Strategy Pattern: Move Data Up

Relative Data Access Latencies- CPU Registers (1)- L1 Cache (1-2)- L2 Cache (6-10)- Main memory (25-50)



Batching: Do it Once


Batching: Maximize Data Locality


Let’s Dig In

Relative Data Access Latencies, Fastest to Slowest- CPU Registers (1)- L1 Cache (1-2)- L2 Cache (6-10)- Main memory (25-100)



Network

If you can’t Move Data Up, minimize accesses


Network


Souders Performance Rules 1) Make fewer HTTP requests- Avoid going halfway to the moon whenever possible


Network



2) Use a content delivery network- Edge caching gets data physically closer to the user


Network



2) Use a content delivery network- Edge caching gets data physically closer to the user

3) Add an expires header- Instead of going halfway to the moon (Network),

climb Godzilla (RAM) or go 40% of the way around the Earth (Disk) instead


Network: Packets and Latency

Less data = less packets = less packet loss = less latency


Network

1) Make fewer HTTP requests 2) Use a content delivery network 3) Add an expires header 4) Gzip components


Disk: Falling of the Latency Cliff


Jim Gray, Microsoft 2006

Tape is DeadDisk is TapeFlash is DiskRAM Locality is King


Strategy: Move Up: Disk to RAM

RAM gets you above the exponential latency line- Linear cost and power consumption = $$$

Main memory (25-50)Hard drive (1e7)


Strategy: Avoidance: Bloom Filters

- Probabilistic answer to question if a member is in a set- Constant time via multiple hashes- Constant space bit string

- Used in BigTable, Cassandra, Squid


In Memory Indexes

Haystack keeps file system indexes in RAM- Cut disk access per image from 3 to 1

Search index compression GFS master node prefix compression of names


Managing Gigabytes -Witten, Moffat, and Bell


SSDs

Disk SSD

I/O Ops / Sec ~ 180 - 200 (15K RPM) ~ 70 - 100

~ 10K - 100K

Seek times ~ 7 - 3.2 ms ~ 0.085 - 0.05 ms

SSDs < 1/5th power consumption of spinning disk


Sequential vs. Random Disk Access

- James Hamilton


1TB Sequential Read


1TB Random Read

Sunday Monday Tuesday Wednesday

Thursday

Friday Saturday

1 2 3 4 5 6 7

8 9 10 11 12 13 14

15 Done!


Strategy: Batching and Streaming

Fewer reads/writes of large contiguous chunks of data- GFS 64MB chunks


Strategy: Batching and Streaming

Fewer reads/writes of large contiguous chunks of data- GFS 64MB chunks

Requires data locality- BigTable app specified data layout and compression


The CPU


“CPU Bound”

Data in RAM CPU access to that data


The Memory Wall


Latency Lags Bandwidth

-Dave Patterson


Multicore Makes It Worse!

More cores accelerates the rate of divergence- CPU performance doubled 3x over the past 5 years- Memory performance doubled once


Evolving CPU Memory Access Designs

Intel Nehalem integrated memory controller and new high-speed interconnect

40 percent shorter latency and increased bandwidth, 4-6x faster system


More CPU evolution

Intel Nehalem-EX - 8 core, 24MB of cache, 2 integrated memory controllers- ring interconnect on-die network designed to speed

the movement of data among the caches used by each of the cores

IBM Power 7- 32MB Level 3 cache

AMD Magny-Cours - 12 cores, 12MB of Level 3 cache


Cache Hit Ratio


Cache Line Awareness

Linked list - Each node as a separate allocation is Bad




Hash table- Reprobe on collision with stride of 1





Stack allocation- Top of stack is usually in cache, top of the heap is

usually not in cache






usually not in cache Pipeline processing- Stages of operations on a piece of data do them all at

once vs. each stage separately






usually not in cache Pipeline processing- Stages of operations on a piece of data do them all at

once vs. each stage separately Optimize for size - Might be faster execution than code optimized for speed


Cycles to Burn

1) Make fewer HTTP requests 2) Use a content delivery network 3) Add an expires header 4) Gzip components- Use excess compute for compression


Datacenter


Datacenter Storage HeiracrchyStorage hierarchy: a different view

A bumpy ride that has been getting bumpier over time- Jeff Dean, Google


Intra-Datacenter Round Trip

x 500,000

~500 miles~NYC to Columbus, OH


Datacenter Level Systems

Facebook Cassandra

Google BigTable

memcached

Redis Project Voldemort

Yahoo Sherpa

Sawzall / Pig

Google File System

RethinkDB

MonetDB

HBaseFacebook Haystack


Memcached Facebook Optimizations

- UDP to reduce network traffic - Less Packets



- UDP to reduce network traffic - Less Packets- One core saturated with network interrupt handing- opportunistic polling of the network interfaces and

setting interrupt coalescing thresholds aggressively - Batching





- Contention on network device transmit queue lock, packets added/removed from the queue one at a time- Change dequeue algorithm to batch dequeues for

transmit, drop the queue lock, and then transmit the batched packets







- More lock contention fixes







- More lock contention fixes

- Result 200,000 UDP requests/second with average latency of 173 microseconds


Google BigTable

Table contains a sequence of blocks- block index loaded into memory - Move Up


Google BigTable


Table can be completely mapped into memory - Move Up


Google BigTable


Table can be completely mapped into memory - Move Up Bloom filters hint for data - Move Up


Google BigTable


Table can be completely mapped into memory - Move Up Bloom filters hint for data - Move Up Locality groups loaded in memory - Move Up, Batching- Clients can control compression of locality groups


Google BigTable



2 levels of caching - Move Up- Scan cache of key/value pairs and block cache


Google BigTable



2 levels of caching - Move Up- Scan cache of key/value pairs and block cache

Clients cache tablet server locations- 3 to 6 network trips if cache is invalid - Move Up


Facebook Cassandra

Bloom filters used for keys in files on disk - Move Up


Facebook Cassandra

Bloom filters used for keys in files on disk - Move Up Sequential disk access only - Batching Append w/o read ahead


Facebook Cassandra

Bloom filters used for keys in files on disk - Move Up Sequential disk access only - Batching Append w/o read ahead Log to memory and write to commit log on dedicated disk -

Batching


Facebook Cassandra


Batching Programmer controlled data layout for locality - Batching


Facebook Cassandra


Batching Programmer controlled data layout for locality - Batching

Result: 2 orders of magnitude better performance than MySQL


Move the Compute to the Data: YQL Execute


From the Browser Perspective

Performance bounded by 3 things:



Performance bounded by 3 things:- Fetch time- Unless you’re bundling everything it is a cascade of

interdependent requests, at least 2 phases worth




interdependent requests, at least 2 phases worth- Parse time- HTML- CSS- Javascript




interdependent requests, at least 2 phases worth- Parse time- HTML- CSS- Javascript

- Execution time- Javascript execution- DOM construction and layout- Style application


Recap

Move Data Up- Caching- Compression- If You Can’t Move All The Data Up- Indexes- Bloom filters

Batching and Streaming- Maximize locality


Take 2 And Call Me In The Morning

An Engineer’s Guide to Bandwidth- http://developer.yahoo.net/blog/archives/2009/10/

a_engineers_gui.html High Performance Web Sites- Steve Souders

Even Faster Web Sites- Steve Souders

Managing Gigabytes: Compressing and Indexing Documents and Images- Witten, Moffat, Bell

Yahoo Query Language (YQL)- http://developer.yahoo.com/yql/


http://developer.yahoo.net/blog/archives/2009/10/a_engineers_gui.html




http://developer.yahoo.com/yql/

http://developer.yahoo.com/yql/

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